Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join a dynamic team to build and maintain data science infrastructure.
- Company: SPG Resourcing values diversity and fosters an inclusive workplace.
- Benefits: Enjoy competitive salary, remote work options, and a collaborative culture.
- Why this job: Be part of innovative projects that transition models from research to production.
- Qualifications: Proficiency in Python, Databricks, Azure, and strong software engineering skills required.
- Other info: Open to diverse backgrounds; reasonable accommodations provided throughout the hiring process.
The predicted salary is between 42000 - 84000 £ per year.
This position is for an experienced Machine Learning Engineer to join a newly established data science team. The primary focus is on building and maintaining the infrastructure to support the full data science lifecycle from data ingestion to model deployment, monitoring, and upgrades within Azure and Databricks environments. The engineer will work closely with data scientists in a collaborative, cross-functional setting, helping transition models from research into production.
Key Responsibilities:
- Own and develop deployment frameworks for data science services.
- Ownership of the deployment framework for all data science services.
- Oversight of how data will flow into the data science life cycle from the wider business data warehouse.
- Oversight of the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when we move to production.
- Automate the data science pipeline (data prep to deployment).
- Collaborate with cross-functional teams to ensure smooth productionization of models.
- Write clean, production-ready Python code.
- Apply software engineering best practices, CI/CD, TDD.
Required Skills:
- Proficiency in Python, Databricks, and Azure.
- Experience with deployment tools (e.g., AKS, managed endpoints).
- Strong software engineering background (CI/CD, VCS, TDD).
- Ability to integrate ML into business workflows.
Desirable:
- Background in quantitative disciplines (math, stats, physics).
- Experience in finance, insurance, or ecommerce.
- Familiarity with ML frameworks like TensorFlow, XGBoost, and SKLearn.
If this sounds like something you are interested in, please get in contact: thomas.deakin@spgresourcing.com
SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process.
Machine Learning Engineer employer: SPG Resourcing
Contact Detail:
SPG Resourcing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with Azure and Databricks, as these are key platforms for the role. Consider taking online courses or tutorials to deepen your understanding of how to deploy machine learning models in these environments.
✨Tip Number 2
Showcase your experience with CI/CD and TDD in your discussions. Be prepared to discuss specific projects where you've implemented these practices, as they are crucial for maintaining high-quality code in production.
✨Tip Number 3
Network with professionals in the data science field, especially those who work with machine learning deployment. Attend meetups or webinars to connect with others and gain insights into best practices and industry trends.
✨Tip Number 4
Prepare to discuss how you can integrate machine learning into business workflows. Think of examples from your past experiences where you've successfully collaborated with cross-functional teams to transition models from research to production.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, Databricks, and Azure. Include specific projects where you've developed deployment frameworks or automated data science pipelines.
Craft a Strong Cover Letter: In your cover letter, emphasise your collaborative experience with cross-functional teams and your ability to transition models from research to production. Mention any relevant experience in finance, insurance, or ecommerce.
Showcase Relevant Skills: Clearly outline your proficiency in CI/CD, TDD, and version control systems. If you have experience with ML frameworks like TensorFlow or XGBoost, make sure to include that as well.
Proofread Your Application: Before submitting, double-check your application for any spelling or grammatical errors. A clean, professional presentation can make a significant difference in how your application is perceived.
How to prepare for a job interview at SPG Resourcing
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python, Databricks, and Azure. Bring examples of past projects where you've successfully implemented these technologies, especially in deploying machine learning models.
✨Understand the Data Science Lifecycle
Familiarise yourself with the full data science lifecycle, from data ingestion to model deployment. Be ready to explain how you would automate this process and ensure smooth transitions from research to production.
✨Emphasise Collaboration
Highlight your experience working in cross-functional teams. Discuss how you’ve collaborated with data scientists and other stakeholders to ensure successful model productionisation.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving skills. Practice explaining your thought process when faced with challenges in deploying machine learning models or automating workflows.
